Autonomous mobile system

ABSTRACT

An autonomous mobile system for a mobile body which moves while localizing itself in a space includes a shape detector and travel distance detector for measuring whether objects are present in set regions determined by dividing a three-dimensional space into a plurality of segments, a storage device into which map data is stored that indicates a region of the set regions that has been set as having a stationary object in the region, a determining section that determines, from frequency of the object detection by the shape detector during a predetermined time for each of the set regions, whether the object that has been detected in each set region is a stationary object or a moving object, and a localizer that localizes a vehicle by matching the region that the determining section has determined to have a stationary object in the region, and the map data.

TECHNICAL FIELD

The present invention relates to an autonomous mobile system for amobile body which moves while localizing itself in a space.

BACKGROUND ART

There is an autonomous mobile system designed for a mobile body (such asvehicle) that moves while localizing itself in a space. Such anautonomous mobile system causes the mobile body to move to a destinationwhile referring to a map generated from the data that has been measuredby a measuring device(s) such as an internal sensor (a sensor formeasuring a state of the mobile body itself) and/or external sensor (asensor needed for the mobile body to measure a state of surroundingswhile moving).

This kind of autonomous mobile systems fall into two major types. Onetype, disclosed in JP-2004-110802-A), causes the mobile body to moveautonomously by recognizing landmarks and/or other specific shapes byuse of a camera as an external sensor, and generating a map. The othertype, disclosed in JP-2008-276348-A, causes the mobile body to moveautonomously as follows: using a laser range scanner as an externalsensor, the mobile body first matches (superposes) sequentially theshape data of a peripheral object that has been acquired at the currenttime of day, to (upon) the shape data of the peripheral object that wasacquired at an immediately previous point in time at a positiondifferent from a current position of the mobile body, then extends theregion where the shape data of the peripheral object has been measured,and thus generates a map.

PRIOR ART LITERATURE Patent Documents

-   Patent Document 1: JP-2004-110802-A-   Patent Document 2: JP-2008-276348-A

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

As described above, conventional autonomous mobile systems have realizedthe localization of the respective vehicles by matching (superposing)the shape data of a peripheral object, measured using a measuring devicesuch as a laser range scanner or stereo camera, to (upon) a map withinwhich the shape of the peripheral object is stored beforehand.

In a traveling environment of the mobile body, however, there existother mobile bodies, including peripheral vehicles, pedestrians,bicycles, grit and dust, fallen leaves, animals, and other movableobjects (e.g., tables, chairs, and planters). If the shape data obtainedby measuring the shapes of these peripheral mobile bodies is matched tothe map, the peripheral mobile bodies may not be properly superposedupon a shape of the map since the peripheral mobile bodies areoriginally not included in the map. Such mismatching is likely toincrease a localizing error, thus cause the mobile body with theautonomous mobile system to lose sight of a target path as well ascontrol of localization, and thus render the mobile body difficult tocontinue the movement.

An object of the present invention is to provide an autonomous mobilesystem that enables highly accurate detection of positions, even in anenvironment with mobile objects therein.

Means for Solving the Problem

In order to achieve the above object, an aspect of the present inventionis an autonomous mobile system for a mobile body which moves whilelocalizing itself in a space, the system including: measuring means formeasuring whether objects are present in each of regions determined bydividing the space into a plurality of segments according to apredetermined rule; storage means into which map data is stored thatindicates a region of the determined regions that has been set as havinga stationary object in the region; means for determining, from frequencyof the object detection by the measuring means during a predeterminedtime for each of the determined regions, whether the object that hasbeen detected in the region is a stationary object or a moving object;and means for localizing the mobile body by matching the region havingtherein the stationary object which the determining means has determinedto be present, and the region that was set in the map data as having astationary object in the region.

Effects of the Invention

In the autonomous mobile system according to an aspect of the presentinvention, peripheral objects that have been detected are eachdetermined to be either a stationary object or a moving object. Thisenables highly accurate matching, even in an environment having at leastone moving object therein, and hence enables the mobile body to reach adestination without losing sight of a target path as well as control oflocalization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an autonomous mobile systemaccording to a first embodiment of the present invention.

FIG. 2 is a flowchart that shows details of processing which theautonomous mobile system executes according to the first embodiment ofthe present invention.

FIG. 3 is a diagram showing the way a shape detector 2 measures shapesof objects present around a vehicle “v”.

FIG. 4 is a diagram showing a plurality of sets of shape data that haveeach been measured at the different time of day during a predeterminedlength of time, a, by the shape detector 2.

FIG. 5 is a detailed flowchart of a process which an operation countingcalculation section 9 according to the first embodiment of the presentinvention conducts.

FIG. 6 is a diagram showing, in three-dimensional voxels “b”,three-dimensional shapes “d” that the shape detector 2 measured at timet−1.

FIG. 7 is a diagram showing, in other three-dimensional voxels “b”, thethree-dimensional shapes “d” that the shape detector 2 measured at timet.

FIG. 8 is a detailed flowchart of a process which a map updating section7 according to the first embodiment of the present invention conducts.

FIG. 9 is a diagram showing a map “r” (map data) as generated accordingto the first embodiment of the present invention.

FIG. 10 is a schematic block diagram of an autonomous mobile systemaccording to a second embodiment of the present invention.

MODE FOR CARRYING OUT THE INVENTION

Hereunder, embodiments of the present invention will be described withreference to the accompanying drawings. While a wheeled vehicle “v”(e.g., a motor vehicle) as a mobile body will be taken by way of examplein the description of the embodiments, the invention can also be appliedto, for example, mobile bodies equipped with crawlers, and robotsequipped with legs, and a form or pattern in which the mobile bodiesmove is not limited.

FIG. 1 is a schematic block diagram of an autonomous mobile systemaccording to a first embodiment of the present invention. The autonomousmobile system shown in FIG. 1 includes an onboard unit 1 a, which ismounted on the vehicle “v”, and a management unit 1 b, which is mountedin a management terminal (e.g., a computer) present in a building or anyother management facilities. The onboard unit 1 a and management unit 1b shown in FIG. 1 are connected to each other via a wireless network, toconduct data communications via the network.

The onboard unit 1 a includes a shape detector 2, a travel distancedetector 3, an operation counting calculation section 9, a determiningsection 4, a localizer 5, a travel control section 6, an arithmeticprocessing unit, for example a CPU (not shown), intended to run variouscontrol programs, and a first storage device 10 a (e.g., ROM, RAM, HDD,and/or flash memory) intended for storage of various data including thecontrol programs.

The shape detector 2, travel distance detector 3, and operation countingcalculation section 9 in the first embodiment of the present inventionfunction as measuring means to measure the presence/absence of objectsin regions determined by dividing into a plurality of segments accordingto a predetermined rule a three-dimensional space in which the vehicle“v” travels (hereinafter, the regions may be referred to as “setregions” or simply as “regions”). As will be detailed later herein,closed regions of a cubic form that are obtained by dividing thethree-dimensional space by three-dimensional voxels of predetermineddimensions are used as the set regions in the present embodiment.

The shape detector (shape detection means) 2 is a device for detectingshapes of buildings, trees, terrains (e.g., hills and cliffs), and otherstationary objects present around the vehicle “v”, and shapes of otherperipheral vehicles, pedestrians, bicycles, grit and dust, fallenleaves, animals, and other movable objects (e.g., tables, chairs, andplanters) present around the vehicle “v”. The shape detector 2 can be,for example, a laser range scanner, a stereo camera, or a time-of-flight(TOF) distance image camera. The shapes of the peripheral objects thathave been measured by the shape detector 2 are input with theirmeasuring time to the first storage device 10 a and stored as shapedata.

The travel distance detector (travel distance detection means) 3 is adevice for detecting a distance through which the vehicle “v” has moved.In the present embodiment, the total amount of wheel rotation of thevehicle “v” is calculated to detect a cumulative travel distance throughwhich the vehicle “v” has moved from a position at which it existed apredetermined control period ago (e.g., one period ago), to a currentposition of the vehicle “v”. For example, a known method (referencedocument: J. Borenstein and L. Feng, Gyrodometry: “A New Method forCombining Data from Gyros and Odometry in Mobile Robots”, Proc. of ICRA'96, 1996) is useable as this kind of detection method. The knowndetection method uses a combination of inertial sensors and gyrosensors,called the Inertial Measurement Unit (IMU). The travel distance that thetravel distance detector 3 has detected is input to the first storagedevice 10 a and stored as shape data.

The operation counting calculation section (operation countingcalculation means) 9 executes the process of calculating, for each ofthe set regions which were determined by dividing beforehand thethree-dimensional space in which the vehicle “v” was planned to move,the number of times the shape detector 2 has measured and detected thepresence/absence of at least one object at the different time of day(i.e., the measurement count), and the number of times the shapedetector 2 has actually detected at least one object in each of the setregions during the measurements (i.e., the detection count). What is tobe reiterated here is that the measurement count refers to how often theshape detector 2 has conducted measurements for detecting at least oneobject in each of a predetermined number of set regions, and that thedetection count refers to how often the shape detector 2 has actuallydetected at least one object in each of the predetermined number of setregions during the measurements. The measurement count increases,irrespective of detection results, and the detection count increasesonly when at least one object is detected.

Although details are described later herein, the operation countingcalculation section 9 in the present embodiment calculates operationcount data (measurement count data and detection count data) from theshape data, travel distance data, and region segment data stored withinthe first storage device 10 a. The measurement count and detection countthat have been calculated by the operation counting calculation section9 are input to the first storage device 10 a and stored as themeasurement count data and the detection count data (the two sets ofdata may be hereinafter termed collectively as operation count data).

The determining section 4 executes the process of determining, fromfrequency of the object detection by the shape detector 2 during apredetermined time (a time interval from the current time of day to theimmediately previous time) for each of the determined regions (i.e., foreach three-dimensional voxel), whether at least one of the objects whichwere detected in the determined region is a stationary object or amoving object. Although details are described later herein, thedetermining section 4 in the present embodiment conducts the calculationof the frequency of object detection, based on the operation count data(measurement count and detection count) that the vehicle “v” obtainedduring the predetermined time from the measuring means (the shapedetector 2, the travel distance detector 3, and the operation countcalculating section 9). In addition, the determining section 4determines, from the calculated frequency of object detection, whetherat least one object in each of the set regions is a stationary object ora moving object. Determination results by the determining section 4 areinput to the first storage device 10 a and stored as determination data.

The shape data detected by the shape detector 2, the travel distancedata detected by the travel distance detector 3, the operation countdata calculated by the operation count calculating section 9, thedetermination data calculated by the determining section 4, and regionsegment data are stored in the first storage device 10 a. The regionsegment data here, which indicates divisions of the set regions used toobtain the operation count data, is data that indicates in what form thethree-dimensional space where the vehicle “v” moves is divided in theplurality of closed regions (set regions). In the present embodiment,where in the three-dimensional space the respective three-dimensionalvoxels are positioned is stored. In which region the objects or partthereof is positioned can be determined by associating the shape dataand the region segment data with each other.

The localizer (localizing means) 5 is a section that executes theprocess of localizing the vehicle “v” by matching the region that thedetermining section 4 determined to have at least one stationary objecttherein (i.e., the shape of the stationary object that the vehicle “v”measured), to the region that was set in map data (described later) of asecond storage device 10 b as the region in which the stationary objectis present. The above matching refers to superposing one of the tworegions upon the other. More specifically, this matching process can beconducted using, for example, a known method (reference document:Takeshi Masuda, Ikuko Okatani (Shimizu), and Ryusuke Sagawa, “Range DataProcessing—A Survey of Shape Model Generation from Multiple RangeImages”, Proc. of the 146th CVIM, 2004).

The travel control section 6 drives the wheels or the like of thevehicle “v” and controls autonomous travel of the vehicle. The travelcontrol section 6 uses calculation results by the localizer 5 to controlthe vehicle “v” so that this vehicle travels to a destination inaccordance with a predefined target path.

The management unit 1 b includes a map updating section 7, a displaycontrol section 8, an arithmetic processing unit (not shown, for examplea CPU) for running various control programs, the second storage device10 b (e.g., ROM, RAM, HDD, and/or flash memory) intended for storage ofvarious data including the control programs, and a display device 11.

The kinds of data stored in the second storage device 10 b include themap data denoting, of all the set regions, only the region that was setas having at least one stationary object therein, and the same regionsegment data, determination data, and operation count data as stored inthe first storage device 10 a.

The map data is the data serving as a basis for the localizer 5 tolocalize the vehicle “v”, and based on detailed prior data measurementsand the like, the map data denotes only the region of the set regionsthat was set as having at least one stationary object therein. The mapdata in the present embodiment, as with the set regions, has a dataformat expressed in three-dimensional voxels, and the stationary objectin the three-dimensional space is defined as a set of voxels. That is tosay, this data format is equivalent to a three-dimensional data formatto which a bitmap format in two-dimensional images was extended. Inaddition, in the present embodiment, the map data is appropriatelyupdated according to particular determination results of the determiningsection 4.

While, in the example of FIG. 1, region segment data, determinationdata, and operation count data are shown as the kinds of data stored inthe first storage device 10 a, other kinds of data may be storedtherein. In addition, while FIG. 1 assumes that input of data from thefirst storage device 10 a to the second storage device 10 b occursautomatically at predetermined intervals of time via the wirelessnetwork, the system may be configured to move data from the firststorage device 10 a to the second storage device 10 b periodicallyeither by wired connection or via any other appropriate storage medium.The map updating section 7 executes the process of updating, on thebasis of the determination results (determination data) by thedetermining section 4, the region that was set in the map data of thesecond storage device 10 b as a region in which at least one stationaryobject is present. In other words, the map updating section 7 in thepresent embodiment executes the process of updating the map data withinthe second storage device 10 b, pursuant to the determination data thatthe vehicle “v” obtained.

The display control section 8 executes the process of receiving the mapdata from the second storage device 10 b and the position data from thelocalizer 5, then calculating, from the two sets of data, a displaysignal needed for the display device 11 to display the map data and anestimated position of the vehicle “v”, and transmitting the displaysignal to the display device 11. The display device 11 displaysperipheral map information and the position of the vehicle “v” inaccordance with the display signal that is input from the displaycontrol section 8. The display device 11 may be constructed integrallywith or independently of the management terminal.

Next, further details of processing by the autonomous mobile systemaccording to the present embodiment are described below referring to theaccompanying drawings. FIG. 2 is a flowchart that shows the details ofprocessing which the autonomous mobile system executes according to thefirst embodiment of the present invention. The autonomous mobile systemexecutes processing shown in the flowchart of FIG. 2, and therebydetermines whether the shape data relating to measured peripheralobjects denotes stationary objects or moving objects. Highly accuratelocalization can therefore be realized, even in a traveling environmentwith moving objects existing therein, such as other vehicles (vehiclesother than the vehicle “v”), pedestrians, bicycles, grit and dust,fallen leaves, animals, tables, chairs, planters, and other movableobjects. Processing in various steps of the flowchart shown in FIG. 2 isdescribed in detail below.

Upon a start of processing, in step S11 the onboard unit 1 a of theautonomous mobile system first uses the shape detector 2 to measurethree-dimensional shapes of objects present around the vehicle “v”(these objects include both of stationary ones and moving ones), andacquire the shape data, and also uses the travel distance detector 3 tomeasure a travel distance of the vehicle “v” and acquire the traveldistance data.

FIG. 3 is a diagram showing the way the shape detector 2 measures theshapes of the objects present around the vehicle “v”. The shape detector2, mounted on the vehicle “v” running through a travelable region (e.g.,a road) in the three-dimensional space, measures the shapes of objectspresent in a measuring range “a” of the detector. The shape dataacquired here includes both of the shapes of stationary objects such asbuildings, and those of moving objects such as vehicles other than thevehicle “v”.

If a laser range scanner is being used as the shape detector 2, objectshidden and concealed behind a detected object are not detected, evenwithin the measuring range “a” defined by a dashed line of asubstantially concentric form from the vehicle “v”. This means that anactual detection range is a region shaded in the measuring range “a” ofFIG. 3, and that the measurement count and detection count for each ofthe set regions included in the shaded region will increase.

FIG. 4 is a diagram showing a plurality of sets of shape data that haveeach been measured at the different time of day during a predeterminedlength of time, a, by the shape detector 2. In this figure, if thecurrent time of day is taken as time “t”, three-dimensional shapes d1are measured at the time “t”. Additionally, three-dimensional shapes d2are measured at time “t−1”, one unit time ago, and three-dimensionalshapes d3 are measured at time “t−2”, one more unit time ago. The oneunit time here is an amount of time needed for the shape detector 2 tocomplete one measurement, and for example, if a laser range scanner of aone-second period is being used as the shape detector 2, the one unittime here is an interval of one second.

After step S11 has ended, the operation count calculating section 9calculates the measurement count and the detection count, for each ofthe set regions.

As shown in FIG. 5, the operation count calculating section 9 firstexecutes pre-processing for each operation count calculation. Morespecifically, in step S21 the operation count calculating section 9refers to the region segment data stored within the first storage device10 a, and divides, by the set regions (three-dimensional voxels), thespace for which the presence/absence of objects that the shape detector2 has detected around the vehicle “v” is to be identified. The spacedivided is equivalent to the shaded region in FIG. 3. In the presentembodiment, each of the set regions is defined in terms of thethree-dimensional voxels obtained by dividing the three-dimensionalspace into a plurality of solids. Each voxel has its dimensions dictatedwith dependence upon dimensions of the objects to be determined to bestationary or moving ones. For example, if the dimensions of the objectsto be determined are those of a motor vehicle, a building, or the like,voxels measuring from about 0.5 to 1.0 meter square will be set. Forconvenience sake, while each of the set regions is defined as a solid inthe present embodiment, these set regions may be defined as other solidshapes such as parallelepipeds, spheres, or triangular pyramids.

The operation count calculating section 9 next converts coordinates ofthe three-dimensional shapes “d” each measured at the different time ofday during the predetermined time by the shape detector 2, according tothe travel distance of the vehicle “v” that was measured by the traveldistance detector 3. After canceling the movement of the vehicle “v” inthis way, in step S22 the operation count calculating section 9 furtherconverts the position coordinates of the three-dimensional shapes “d”from the vehicle coordinate system (the coordinate system fixed for thevehicle “v”) to an absolute coordinate system.

After the conversion, in step S23 the operation count calculatingsection 9 calculates the detection count in accordance with which voxel(set region) on the absolute coordinate system contains each of thethree-dimensional shape “d”. In other words, if the three-dimensionalshapes “d” measured by the shape detector 2 are present in thethree-dimensional voxels neighboring the vehicle “v”, the detectioncount is increased. This process is conducted for the three-dimensionalshapes “d” measured from the current time of day, “t”, to the time ofday, t−α, that is the predetermined time ago (a is a set value). That isto say, this process increases the measurement count for the voxels byup to a count value obtained by dividing a by one unit time of the shapedetector 2.

The operation count calculating section 9 also calculates themeasurement count of the voxels, along with the above calculation of thedetection count, in step S24. The calculation of the measurement countis accomplished by identifying the voxels contained in the region(shaded region in FIG. 3) that the shape detector 2 actually detectedthe existence of objects by measurement, and increasing the measurementcount for the identified voxels by 1. The measurement count anddetection count that the operation count calculating section 9calculated in steps S23, S24 are input to the first storage device 10 aand stored as operation count data.

Upon completion of step S24, processing advances to step S13 shown inFIG. 2. In step S13, the determining section 4 makes independentreference to the measurement count and detection count for each voxelwhich was measured during the predetermined time α, and determineswhether the object detected in each voxel is a stationary one or amoving one. In this step, whether an object exists in the voxel isdetermined first. If an object is determined to be present, whether theobject is a stationary one or a moving one is determined next. If anobject is determined to be absent, the region is determined to be a freespace. When the determining section 4 in the present embodiment conductsthe determination in each voxel, the determining section 4 refers to theoperation count data stored within the first storage device 10 a andcalculates, for each voxel, a rate of the detection count to themeasurement count (i.e., a value of the detection count/measurementcount, and this value may be hereinafter termed the “occupancy score”).If the occupancy score is equal to or greater than a predeterminedthreshold value β (say, 0.8), the object present in the particular voxelis determined to be a stationary object, or if the occupancy score isless than the predetermined threshold value β, the object is determinedto be a moving object, or if the occupancy score is zero, the region isdetermined to be a free space. Determination results on each voxel areinput from the determining section 4 to the first storage device 10 aand stored as determination data.

The occupancy score for the voxels each containing a stationary objectincreases because the measurement of a three-dimensional shape “d” iscontinued from the past to the present. The occupancy score for thevoxels each containing a moving object decreases relative to the above,because the measurement of a three-dimensional shape “d” takes placeonly during part of the time from the past to the present. The occupancyscore for the voxels each determined to be a free space becomes zerobecause of a three-dimensional shape “d” not being measured.

The three-dimensional shapes “d” that were each measured at thedifferent time of day (i.e., time t−1 and time “t”) are shown inthree-dimensional voxels “b” by way of example in FIGS. 6 and 7. Theshapes of a stationary object “c” and a moving object “m” are measuredin both of the examples. Of all the three-dimensional voxels “b” formedby dividing a space, only those having the stationary object “c” presenttherein have a three-dimensional shape “d” measured at both of the timet−1 and the time “t”. Because of this, the occupancy score increases andin process step S13, the object is determined to be a stationary one.For the voxel through which the moving object “m” has moved, on theother hand, because the three-dimensional shape “d” is measured only ateither of the time t−1 and the time “t”, the occupancy score decreases,so in process step S13, the object is determined to be a moving one. Forvoxels without an object, because at neither the time t−1 nor the time“t” is a three-dimensional shape “d” measured, the occupancy scorebecomes zero and in process step S13, those regions are each determinedto be a free space.

Upon completion of step S13, in step S14 the localizer 5 localizes thevehicle “v” by matching the set region having therein the stationaryobject which the determining section 4 determined to be present, and atleast one of the regions which were set in the map data of the secondstorage device 10 b as the regions each having at least one stationaryobject therein.

In this step, in order to identify the region having therein thestationary object which the determining section 4 determined to bepresent, the localizer 5 first refers to the determination data withinthe first storage device 10 a and extracts the three-dimensional shape“d” belonging to the voxel which was determined to have a stationaryobject therein, as the shape of the stationary object. Next, thelocalizer 5 refers to the map data within the second storage device 10 band extracts the three-dimensional shape belonging to the voxel whichwas determined to have a stationary object therein, as the shape of thestationary object. After this, the localizer 5 localizes the vehicle “v”by matching (superposing) the shape of the stationary object to (upon)the map data. In the present embodiment configured as above, since onlythe shape of at least one stationary object, except for that of at leastone moving object, is matched to the map data, the vehicle “v” can belocalized very accurately, even in a traveling environment with movingobjects present therein.

After processing has been executed up to step S14, the position that wascalculated in step S14 may be compared with that of the destination,whereby it may then be determined whether the vehicle has arrived at thedestination, and process control may be returned to the first step(S11). In the present embodiment, however, map updating is continued,which is described below.

After step S14, in step S15 the onboard unit 1 a transfers thedetermination data that was calculated in step S13 and stored into thefirst storage device 10 a, to the second storage device 10 b within themanagement unit 1 b via the wireless network. Thus the data indicatingwhether the object in each voxel during the predetermined time α is anstationary object or a moving one is stored into the management unit 1b.

Next on the basis of the determination data that was input in step S15,the map updating section 7 updates the map data that has been stored inthe second storage device 10 b before step S15 was executed in step S16.Step S16 may be executed either when the determination data is receivedfrom the onboard unit 1 a in step S15, or at predetermined intervals oftime other than those at which the determination data is received. Adetailed flowchart of the process which the map updating section 7conducts in step S16 is shown in FIG. 8.

As shown in FIG. 8, in step S31 the map updating section 7 first refersto the determination data and thus adds the frequency with which movingobjects were determined to be present during the predetermined time α,that is, a moving-object score, for each of three-dimensional voxels “g”in the map data. The moving-object scores for the voxels are stored intothe second storage device 10 b as part of the map data. This enablesrelatively high moving-object scores to be assigned to the voxelsarranged in locations that at least one moving object frequentlyappears. Conversely, relatively low moving-object scores will beassigned to the voxels arranged in locations that no moving objectsappear. Voxels falling outside the measuring range of the shape detector2 over a predetermined period of time may each be handled as a freespace by deleting information about stationary objects, andmoving-object scores may be continuously reduced to prevent data in thepast from continuing to remain stored.

After step S31, in step S32 the map updating section 7 refers to thedetermination data, thus updating the regions set in the map data aswhere at least one stationary object is present. For example, when a newbuilding, which was not present during generation of the map data, hasbeen built, mismatching in contents will occur between the determinationdata and the map data. In step S32, therefore, in a case where a voxelthat the determining section 4 has determined to have a stationaryobject therein is not set in the map data as where the stationary objectis present, the setting of the voxel in the map data is updated to matchto the determination data. In other words, the stationary object isadded to that voxel in the map data. Conversely, in a case that a voxelthat is set in the map data as where a stationary object is present isnot determined by the determining section 4 to exist, the setting of thevoxel in the map data is updated to match to the determination data. Inother words, the stationary object is deleted from that voxel in the mapdata. Thus the map data can always be maintained in the latestcondition, even when the construction of a new building or other changesin environment occur.

FIG. 9 shows a map “r” having moving-object scores added during theprocess of the map updating section 7, and having voxels of stationaryobjects updated during the process. Although the map “r” shown in thefigure is actually represented in the form of three-dimensional voxels“g”, this map is depicted in projected form on a parallel plane as aplan view in FIG. 9. As shown in an upper half of FIG. 9, the shapes ofstationary objects are stored in the map “r”. In addition, as shown in alower half of FIG. 9, each of the three-dimensional voxels “g”, the dataformat of the map, has information of a moving-object score “p”. Forexample, a three-dimensional voxel “g” corresponding to a road on whichother vehicles, grit and dust, and/or other moving objects frequentlyappear has a relatively high moving-object score “p”, and athree-dimensional voxel “g” corresponding to a stationary object such asa building or tree has a relatively low moving-object score “p”.

The localizer 5 can localize the vehicle “v” more accurately byutilizing the moving-object scores “p” when the localizer 5 conductsmatching to the map data in Step 14. For example, as described in areference document (Szymon Rusinkiewicz, Marc Levoy, “Efficient Variantsof the ICP Algorithm”, Proc. of International Conference on 3-D DigitalImaging and Modeling, 2001), a reciprocal of a moving-object score “p”may be used to denote reliability (weight), and the stationary objectwhose shape is measured at a location of low reliability (a location atwhich moving objects appear very frequently) may be set and regarded asa moving object. Use of this matching method, therefore, enables highlyaccurate localization even under the traveling environment where movingobjects exist.

Additionally, in the present embodiment, determination data has beeninput to the second storage device 10 b and map data may be updated toincorporate the determination data. Instead, however, the operationcount data that was calculated in step S12 may be input to the secondstorage device 10 b and after such a determination process as conductedin process step S13, map data may be updated to incorporate a result ofthe determination. If the map data is updated in this manner, theexistence of stationary objects even over a long period of time can beincorporated into the map data accurately. The threshold value used herein the determination process may differ from the value of β used in stepS13. In other words, a threshold value greater than that used in stepS13 is preferably used for enhanced extraction accuracy of stationaryobjects.

Referring back to the flowchart of FIG. 2, after the map data updateprocess (step S16), in step S17 the display control section 8 of themanagement unit 1 b displays the position of the vehicle “v” that wasestimated in step S14, and the map data that was updated in step S16, toan operator via the display device 11 included in the management unit 1b.

In step S18, on the basis of the position of the vehicle “v” that wasestimated in step S14, the travel control section 6 controls theautonomous travel of the vehicle “v” so that the vehicle can travel tothe destination in accordance with the predefined target path. Theautonomous travel here can be made by use of a known method (referencedocument: Jun Ohta, Daisuke Kurabayashi, and Tamio Arai, “AnIntroduction to Intelligent Robots”, Corona Publishing Co., Ltd. 2001).

In step S19, the travel control section 6 determines whether thedestination has been reached. If the destination is not reached, controlis returned to the process in step S11. If the destination is reached,the successive process steps are completed, whereby the autonomousmobile system 1 causes the vehicle to determine the measured shape dataof the peripheral objects to be that of stationary objects and movingobjects, and hence to reach the destination by conducting highlyaccurate localization even under a traveling environment with movingobjects present therein.

In the present embodiment configured as above, therefore, sinceperipheral objects can be determined to be stationary ones and movingones, highly accurate matching can be conducted even in a travelingenvironment with moving objects present therein, and thus, manydifferent kinds of moving objects, including motor vehicles, can be madeto reach respective destinations without losing sight of target paths aswell as control of localization.

An example in which the second storage device 10 b, the map updatingsection 7, the display control section 8, and the display device 11 aremounted in the management unit (terminal unit 1 b) has been described inthe above embodiment, but all processing may be done on the vehicle “v”side with these elements mounted on the vehicle (onboard unit 1 a). Inaddition or alternately, the system may be configured so that the shapedetector 2, the travel distance detector 3, and the travel controlsection 6 are mounted on the vehicle “v” and so that other constituentelements are mounted in the management terminal. In this case, thesystem may be configured so that the data within the detectors 2, 3 willbe transmitted from the vehicle “v” to the management unit 1 b by meansof data communications such as wireless communications, and so thatprocessing that follows will be conducted by the management unit 1 b anddata on the estimated position of the vehicle will be fed back from themanagement unit 1 b.

Furthermore, while in the above embodiment the data from the shapedetector 2 and travel distance detector 3 mounted on one vehicle “v” hasbeen used for the localization of the vehicle, data measured by aplurality of vehicles may be used for purposes such as localizing thevehicle. This case is described below.

FIG. 10 is a schematic block diagram of an autonomous mobile systemaccording to a second embodiment of the present invention. Theautonomous mobile system shown in FIG. 10 includes at least onboardunits 1 aa, 1 ab, 1 ac mounted on a plurality of vehicles, v1, v2, v3,respectively, and a management unit 1 b mounted in a managementterminal. The same reference number is assigned to each of the sameelements as in the previous figures, and description of these elementsmay be omitted below.

The onboard units 1 aa, 1 ab, 1 ac shown in FIG. 10 include shapedetectors 2 a, 2 b, 2 c, travel distance detectors 3 a, 3 b, 3 c,storage devices 10 aa, 10 ab, 10 ac, and travel control sections 6 a, 6b, 6 c, respectively. After the detectors 2 a, 2 b, 2 c, 3 a, 3 b, 3 chave detected shape data and travel distance data, the onboard unitsoutput the two kinds of data to the storage device 10 b of themanagement unit 1 b. The management unit 1 b includes an operation countcalculating section 9, a determining section 4, and a localizer 5.Process steps S12 to S17 in FIG. 2 are executed in the management unit 1b. Process steps S18, S19 are executed in each of the vehicles v1, v2,v3.

Briefly, in the present embodiment, a plurality of sets of shape datathat have each been measured by each of the shape detectors 2 mounted onthe plurality of vehicles “v” at the different time of day during apredetermined time are used for the system to determine whether objectsare stationary ones or moving ones. In the thus-configured system,reference can be made to a greater amount of shape data than thatacquired by one vehicle, so that the system can conduct thestationary/moving object determination more accurately. If the systemprocesses data in substantially the same manner, the system may adopt aconfiguration other than that shown in FIG. 10.

While the above has described the application of the present inventionto the autonomous mobile system for the mobile body which moves whilelocalizing itself, the invention can also be applied to systemsrequiring the localization of the mobile body, even if the mobile bodydoes not autonomously move.

DESCRIPTION OF REFERENCE NUMBERS

-   1 a Onboard unit-   1 b Management unit-   2 Shape detector (Measuring means)-   3 Travel distance detector (Measuring means)-   4 Determining section (Determining means)-   6 Travel control section-   7 Map updating section (Map updating means)-   8 Display control section-   9 Operation count calculating section-   10 a First storage device (First storage means)-   10 b Second storage device (Second storage means)-   11 Display device (Display means)

The invention claimed is:
 1. An autonomous mobile system for a mobilebody which moves while localizing itself in a space, the systemcomprising: a processor; a measuring unit that measures whether objectsare present or absent in each of regions determined by dividing thespace into a plurality of segments according to a predetermined rule; astorage device, coupled to the processor, that stores map dataindicating a region of the determined regions that has been set ashaving a stationary object in the region; wherein the processor includesa determining unit that determines, from frequency of the objectdetection by the measuring unit during a predetermined time for each ofthe determined regions, whether the object that has been detected in theregion is a stationary object or a moving object; and a localizer thatlocalizes the mobile body by matching the region having therein thestationary object which the determining unit has determined to bepresent, and the region that was set in the map data as having astationary object in the region.
 2. The autonomous mobile systemaccording to claim 1, wherein: the storage device stores thereinmeasurement count data and detection count data, the measurement countdata denoting, for each of the regions, the number of times themeasuring unit has measured the presence/absence of an object at thedifferent time of day during the predetermined time, the detection countdata denoting, for each of the regions, the number of times themeasuring unit has detected an object at the different time of dayduring the predetermined time; and the determining unit calculates thefrequency of the object detection, based upon the measurement count dataand the detection count data for each of the determined regions.
 3. Theautonomous mobile system according to claim 2, wherein: the determiningunit determines whether the objects detected in the regions arestationary objects or moving objects, depending upon a magnitude of arate of the detection count for each of the regions to the measurementcount.
 4. The autonomous mobile system according to claim 3, wherein:the determining unit determines that if the magnitude of the rate isequal to or greater than a threshold β, the object present in the regionis a stationary object, and that if the magnitude of the rate is lessthan a threshold β, the object present in the region is a moving object.5. The autonomous mobile system according to claim 1, furthercomprising: a map updating unit that updates, in accordance with aresult of the determination by the determining unit, the region set inthe map data as having a stationary object in the region.
 6. Theautonomous mobile system according to claim 1, wherein: the measuringunit, the determining unit, and the localizer are mounted on the mobilebody; the storage device is mounted in the mobile body; and the mobilebody exchanges data with a management terminal through wirelesscommunication.
 7. The autonomous mobile system according to claim 1,wherein: the measuring unit is mounted on a plurality of mobile bodiesincluding the mobile body; and the storage device stores thereinmeasurement count data and detection count data, the measurement countdata denoting, for each of the regions, the number of times themeasuring unit has measured the presence/absence of an object at thedifferent time of day during the predetermined time, the detection countdata denoting, for each of the regions, the number of times themeasuring unit has detected an object at the different time of dayduring the predetermined time.
 8. The autonomous mobile system accordingto claim 1, further comprising: a display device that displays aposition of the mobile body which has been localized by the localizer,and the map data.
 9. The autonomous mobile system according claim 1,wherein: each of the regions is a region determined by dividing thespace into a plurality of parallelepipeds.
 10. The autonomous mobilesystem according to claim 1, wherein: the measuring unit detects shapedata of objects present around the mobile body, and detects a traveldistance of the mobile body; and the measuring unit measurespresence/absence of objects in each of the regions in accordance withthe shape data and the travel distance.